This research focuses on optimizing escape routes in emergency scenarios involving active threats to minimize risks to lives and property. We evaluate the effectiveness of four algorithms Ant Colony Optimization (ACO), Bellman-Ford, Dijkstra, and A* in determining the most efficient escape routes across 50 maps. The primary objective is to identify paths that not only provide the shortest escape route but also reduce potential exposure to the threat. Our results demonstrate that the ACO algorithm consistently delivers the best performance, offering optimized escape routes that meet these criteria more effectively than the other algorithms. By prioritizing safety and minimizing exposure, this approach contributes valuable insights to emergency response planning in high-risk situations, potentially mitigating harm to individuals and property. The comparison and analysis of these algorithms provide a foundation for future advancements in intelligent navigation systems for crisis management.